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--- |
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task_categories: |
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- image-classification |
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tags: |
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- waste |
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- classification |
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pretty_name: waste-cl |
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--- |
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# Dataset Card for waste classifier |
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This dataset contains waste images in different categories: |
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- cardboard |
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- compost |
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- glass |
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- metal |
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- paper |
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- plastic |
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- trash |
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### Dataset Description |
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- **Curated by:** Rootstrap |
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- **License:** MIT |
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### Dataset Sources |
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Data is a combination of [Trashnet](https://github.com/garythung/trashnet) dataset plus more images obtained by internet search. |
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Paper: [Classification of Trash for Recyclability Status](https://cs229.stanford.edu/proj2016/report/ThungYang-ClassificationOfTrashForRecyclabilityStatus-report.pdf) |
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## Uses |
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The dataset can be used for waste classification or other type of project. |
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### Direct Use |
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This dataset is used to build a waste classifier for categorizing different types of waste, being able to correctly throw the trash in the corresponding trash can at our office. |
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{{ direct_use | default("[More Information Needed]", true)}} |
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## Dataset Structure |
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The data is already split in train and test folders. |
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Inside each folder contains one folder for each class. |
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## Dataset Creation |
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### Curation Rationale |
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at Rootstrap, our Machine Learning Engineers are committed to creating awareness of correct waste classification to help the environment. |
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Their determination to make an impact led to the creation of 'RootTrash', an internal AI-powered app to help us recycle correctly. |
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#### Data Collection and Processing |
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Some of the images were obtained using Bing searcher using the api HTTP. |
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You can find the code used to download the images at this [Google Colab](https://colab.research.google.com/drive/1JvAYFx1DIEi1MMyI-tuCfE2eHMSKisKT?usp=sharing). |
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#### Who are the source data producers? |
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Thung, G., & Yang, M. (2016). Classification of Trash for Recyclability Status. |
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## Bias, Risks, and Limitations |
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Current model has been trained mostly with internet images and most of them has white background. This might be an issue when testing with real images. |
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In the future, the dataset will be extended with the photos taken through the app. |
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### Recommendations |
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Integrate this model with a detection model such as [rootstrap-org/waste-detector](https://huggingface.co/rootstrap-org/waste-detector) |
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